
E-GRU [1]}. E-GRU thresholds a sequential classifier's output probability in real time. When the predicted probability \(\hat{y}\)  surpasses a threshold \(\alpha \) , \(\hat{y}\)  is used to classify the series, ignoring all future observations. A hidden state represents the streaming series and is updated whenever a new observation arrives. Each time it is updated, the hidden state is passed to a neural network that predicts \(\hat{y}\) .

EARLIEST [2]}. Similar to E-GRU, EARLIEST models ongoing time series with an RNN. Whenever a new observation arrives, a new hidden state is computed. Similar to Stop&Hop, a halting policy then decides whether or not to stop and classify the series with a neural network. This baseline also helps to ablate the Hopping Policy Network network, as it only chooses between Stop and Wait, ignoring irregularity between observations.

